基于精细化梯度的无线传感器网络汇聚机制及分析

信息汇聚是无线传感器网络的典型传输形态之一.在信息汇聚过程中,网络存在数据流分布内密外疏的不均衡特性.研究发现,在传统跳数模型下,网络数据流分布不仅存在全局不均衡性,而且同层节点内还存在着内疏外密的反向不均衡现象,从而使网络流量分析复杂化.提出一种精细化梯度模型,通过引入加权平均机制,将跳数信息转化为精细梯度信息,并以梯度作为数据汇聚的参考依据.通过理论和仿真分析,精细化梯度模型下网络具有更平稳的网络数据流分布特征,并在通常情况下具有更高的通信效率.

Mechanism and Analysis on Fine-Grain Gradient Sinking Model in Wireless Sensor Networks

Data sinking is one of the typical transmission patterns in WSN (wireless sensor network). There is inherent unbalanced traffic load distribution in such funnel like transmission. A case in hop-based sinking (HBS) model is found more intricate than simple thought that inner nodes burden more forwarding tasks, showing the inverse direction within the same hop level comparing with global trend. With global trend. With a simple weighted average mechanism, a continuous gradient parameter is introduced, which will be dedicated to instructing how to forward data to sink in place of hop count, namely fine-grain gradient sinking (FGS). Through traffic analysis and detailed simulation, in FGS model network turns out to be smoother on traffic load distribution and more efficient on data forwarding than that HBS model.